Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM
This work addresses the need for automated, structured, and interactive medical report generation to assist physicians and reduce errors in radiology, though it is incremental as it builds on existing LLM and detection techniques.
The paper tackles the problem of generating structured chest X-ray reports from images by introducing a prompt-guided method using a pre-trained LLM, which integrates anatomical region detection and clinical context prompts to produce tailored reports, showing strong performance in evaluation metrics.
Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based sentences. We also convert the detected anatomy into textual prompts conveying anatomical comprehension to the LLM. Additionally, the clinical context prompts guide the LLM to emphasize interactivity and clinical requirements. By integrating anatomy-focused sentences and anatomy/clinical prompts, the pre-trained LLM can generate structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. We evaluate using language generation and clinical effectiveness metrics, demonstrating strong performance.